Security is among the top concerns of operating system (OS) developers. A secure runtime environment relies on the OS to correctly configure the memory hardware on which it runs. This is mission-critical as it provides essential security-relevant features and abstractions that ensure the integrity and isolation of untrusted applications running alongside each other. Configuring a platform's memory hardware is not a one-off effort as designers constantly develop new mechanisms for translation and protection with different features and means of configuration. Adapting the OS code to the new hardware is not only a manual, repetitive and time-consuming task, it may also introduce subtle, but security-critical bugs that break security and isolation guarantees.

There exist many mechanisms, ranging from processes to virtual machines, for isolating untrusted computations from each other. Each mechanism explicitly isolates certain resources while, either implicitly or explicitly, sharing the rest. Unfortunately, we lack a comprehensive way to formally and systematically reason about which resources are shared, to what extent they are shared, and how this sharing determines the degree of isolation between any two computations.

Address translation hardware is at the cornerstone of modern computer systems. It provides a wide range of security-relevant features and abstractions such as memory partitioning, address space isolation, and virtual memory. Hardware designers have developed different memory protection schemes with varying features and means of configuration.

Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.